Difference between revisions of "EU-kalat"

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(Initiate conc_pcddf for PFAS disease burden study)
(Initiate conc_pcddf for PFAS disease burden study)
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==== Initiate conc_pcddf for PFAS disease burden study ====
 
==== Initiate conc_pcddf for PFAS disease burden study ====
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 +
This code is similar to preprocess but is better and includes PFAS concentrations from [[:op_fi:PFAS-yhdisteiden tautitaakka]]. It produces data.frame euw that is the EU-kalat + PFAS data in wide format and, for PFAS but not EU-kalat, a sampled value for measurements below the level of quantification.
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<rcode name="preprocess2" label="Preprocess and initiate data.frame euw (for developers only)" embed=1>
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# This is code Op_en3104/preprocess2 on page [[EU-kalat]]
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library(OpasnetUtils)
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library(ggplot2)
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library(reshape2)
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openv.setN(1)
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opts = options(stringsAsFactors = FALSE)
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euRaw <- Ovariable("euRaw", ddata = "Op_en3104", subset = "POPs") # [[EU-kalat]]
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eu <- Ovariable(
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  "eu",
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  dependencies = data.frame(
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    Name=c("euRaw", "TEF"),
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    Ident=c(NA,"Op_en4017/initiate")
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  ),
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  formula = function(...) {
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    out <- euRaw
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    out$Length<-as.numeric(as.character(out$Length_mean_mm))
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    out$Year <- as.numeric(substr(out$Catch_date, nchar(as.character(out$Catch_date))-3,100))
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    out$Weight<-as.numeric(as.character(out$Weight_mean_g))
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    out <- out[,c(1:6, 8: 10, 14:17, 19:22, 18)] # See below
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    #[1] "ﮮTHL_code"            "Matrix"                "POP"                  "Fish_species"       
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    #[5] "Catch_site"            "Catch_location"        "Catch_season"          "Catch_square"       
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    #[9] "N_individuals"        "Sex"                  "Age"                  "Fat_percentage"     
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    #[13] "Dry_matter_percentage" "euRawSource"          "Length"                "Year"               
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    #[17] "Weight"                "euRawResult"         
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    colnames(out@output)[1:13] <- c("THLcode", "Matrix", "Compound", "Fish", "Site", "Location", "Season",
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                                  "Square","N","Sex","Age","Fat","Dry_matter")
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    out@marginal <- colnames(out)!="euRawResult"
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    tmp <- oapply(out * TEF, cols = "Compound", FUN = "sum")
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    colnames(tmp@output)[colnames(tmp@output)=="Group"] <- "Compound"
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    # levels(tmp$Compound)
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    # [1] "Chlorinated dibenzo-p-dioxins" "Chlorinated dibenzofurans"    "Mono-ortho-substituted PCBs" 
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    # [4] "Non-ortho-substituted PCBs" 
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    levels(tmp$Compound) <- c("PCDD","PCDF","moPCB","noPCB")
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    out <- OpasnetUtils::combine(out, tmp)
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    out$Compound <- factor( # Compound levels are ordered based on the data table on [[TEF]]
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      out$Compound,
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      levels = unique(c(levels(TEF$Compound), unique(out$Compound)))
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    )
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    out$Compound <- out$Compound[,drop=TRUE]
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    return(out)
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  }
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)
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eu <- EvalOutput(eu)
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euw <- reshape(
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  eu@output,
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  v.names = "euResult",
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  idvar = c("THLcode", "Matrix", "Fish"), # , "Site","Location","Season","Square","N","Sex","Age","Fat", "Dry_matter","Length","Year","Weight"
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  timevar = "Compound",
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  drop = c("euRawSource","TEFversion","TEFrawSource","TEFSource","Source","euSource"),
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  direction = "wide"
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)
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colnames(euw) <- gsub("euResult\\.","",colnames(euw))
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euw$PCDDF <- euw$PCDD + euw$PCDF
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euw$PCB <- euw$noPCB + euw$moPCB
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euw$TEQ <- euw$PCDDF + euw$PCB
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euw$PFAS <- euw$PFOA + euw$PFOS
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#################### PFAS measurements from Porvoo
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conc_pfas_raw <- EvalOutput(Ovariable(
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  "conc_pfas_raw",
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  data=opbase.data("Op_fi5932", subset="PFAS concentrations"), # [[PFAS-yhdisteiden tautitaakka]]
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  unit="ng/g f.w.")
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)@output
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conc_pfas_raw <- reshape(conc_pfas_raw,
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                        v.names="conc_pfas_rawResult",
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                        timevar="Compound",
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                        idvar=c("Obs","Fish"),
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                        drop="conc_pfas_rawSource",
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                        direction="wide")
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colnames(conc_pfas_raw) <- gsub("conc_pfas_rawResult\\.","",colnames(conc_pfas_raw))
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conc_pfas_raw <- within(conc_pfas_raw, PFAS <- PFOS + PFHxS + PFOA + PFNA)
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conc_pfas_raw$Obs <- NULL
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euw <- orbind(euw, conc_pfas_raw)
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objects.store(euw)
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cat("Data.frame euw stored.\n")
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</rcode>
  
 
Bayesian approach for PCDDF, PCB, OT, PFAS.
 
Bayesian approach for PCDDF, PCB, OT, PFAS.

Revision as of 04:45, 12 March 2021


EU-kalat is a study, where concentrations of PCDD/Fs, PCBs, PBDEs and heavy metals have been measured from fish

Question

The scope of EU-kalat study was to measure concentrations of persistent organic pollutants (POPs) including dioxin (PCDD/F), PCB and BDE in fish from Baltic sea and Finnish inland lakes and rivers. [1] [2] [3].

Answer

Dioxin concentrations in Baltic herring.

The original sample results can be acquired from Opasnet base. The study showed that levels of PCDD/Fs and PCBs depends especially on the fish species. Highest levels were on salmon and large sized herring. Levels of PCDD/Fs exceeded maximum level of 4 pg TEQ/g fw multiple times. Levels of PCDD/Fs were correlated positively with age of the fish.

Mean congener concentrations as WHO2005-TEQ in Baltic herring can be printed out with this link or by running the codel below.

+ Show code

Rationale

Data

Data was collected between 2009-2010. The study contains years, tissue type, fish species, and fat content for each concentration measurement. Number of observations is 285.

There is a new study EU-kalat 3, which will produce results in 2016.

Calculations

Preprocess

  • Preprocess model 22.2.2017 [4]
  • Model run 25.1.2017 [5]
  • Model run 22.5.2017 with new ovariables euRaw, euAll, euMain, and euRatio [6]
  • Model run 23.5.2017 with adjusted ovariables euRaw, eu, euRatio [7]
  • Model run 11.10.2017: Small herring and Large herring added as new species [8]
  • Model rerun 15.11.2017 because the previous stored run was lost in update [9]
  • Model run 21.3.2018: Small and large herring replaced by actual fish length [10]
  • Model run 26.3.2018 eu2 moved here [11]

See an updated version of preprocess code for eu on Health effects of Baltic herring and salmon: a benefit-risk assessment#Code for estimating TEQ from chinese PCB7

+ Show code

Bayes model for dioxin concentrations

  • Model run 28.2.2017 [12]
  • Model run 28.2.2017 with corrected survey model [13]
  • Model run 28.2.2017 with Mu estimates [14]
  • Model run 1.3.2017 [15]
  • Model run 23.4.2017 [16] produces list conc.param and ovariable concentration
  • Model run 24.4.2017 [17]
  • Model run 19.5.2017 without ovariable concentration [18] ⇤--#: . The model does not mix well, so the results should not be used for final results. --Jouni (talk) 19:37, 19 May 2017 (UTC) (type: truth; paradigms: science: attack)
----#: . Maybe we should just estimate TEQs until the problem is fixed. --Jouni (talk) 19:37, 19 May 2017 (UTC) (type: truth; paradigms: science: comment)
  • Model run 22.5.2017 with TEQdx and TEQpcb as the only Compounds [19]
  • Model run 23.5.2017 debugged [20] [21] [22]
  • Model run 24.5.2017 TEQdx, TECpcb -> PCDDF, PCB [23]
  • Model run 11.10.2017 with small and large herring [24] (removed in update)
  • Model run 12.3.2018: bugs fixed with data used in Bayes. In addition, redundant fish species removed and Omega assumed to be the same for herring and salmon. [25]
  • Model run 22.3.2018 [26] Model does not mix well. Thinning gives little help?
  • Model run 25.3.2018 with conc.param as ovariable [27]

+ Show code

Initiate conc_pcddf for PFAS disease burden study

This code is similar to preprocess but is better and includes PFAS concentrations from op_fi:PFAS-yhdisteiden tautitaakka. It produces data.frame euw that is the EU-kalat + PFAS data in wide format and, for PFAS but not EU-kalat, a sampled value for measurements below the level of quantification.

+ Show code

Bayesian approach for PCDDF, PCB, OT, PFAS.

  • Model run 2021-03-08 [28]
  • Model run 2021-03-08 [29] with the fish needed in PFAS assessment

+ Show code

+ Show code

NOTE! This is not a probabilistic approach. Species and area-specific distributions should be created.

+ Show code

Initiate conc_pcddf for Goherr

  • Model run 19.5.2017 [30]
  • Model run 23.5.2017 with bugs fixed [31]
  • Model run 12.10.2017: TEQ calculation added [32]
  • Model rerun 15.11.2017 because the previous stored run was lost in update [33]
  • 12.3.2018 adjusted to match the same Omega for all fish species [34]
  • 26.3.2018 includes length and time as parameters, lengt ovariable initiated here [35]

+ Show code

⇤--#: . These codes should be coherent with POPs in Baltic herring. --Jouni (talk) 12:14, 7 June 2017 (UTC) (type: truth; paradigms: science: attack)

See also

References

  1. A. Hallikainen, H. Kiviranta, P. Isosaari, T. Vartiainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan dioksiinien, furaanien, dioksiinien kaltaisten PCB-yhdisteiden ja polybromattujen difenyylieettereiden pitoisuudet. Elintarvikeviraston julkaisuja 1/2004. [1]
  2. E-R.Venäläinen, A. Hallikainen, R. Parmanne, P.J. Vuorinen: Kotimaisen järvi- ja merikalan raskasmetallipitoisuudet. Elintarvikeviraston julkaisuja 3/2004. [2]
  3. Anja Hallikainen, Riikka Airaksinen, Panu Rantakokko, Jani Koponen, Jaakko Mannio, Pekka J. Vuorinen, Timo Jääskeläinen, Hannu Kiviranta. Itämeren kalan ja muun kotimaisen kalan ympäristömyrkyt: PCDD/F-, PCB-, PBDE-, PFC- ja OT-yhdisteet. Eviran tutkimuksia 2/2011. ISSN 1797-2981 ISBN 978-952-225-083-4 [3]